PySpark Course in Chennai
Get enrolled for the most demanding skill in the world. PySpark Course in Chennai will make your career a new height. We at Besant technologies provide you an excellent platform to learn and explore the subject from industry experts. We help students to dream high and achieve it.
What is PySpark?
PySpark is a Python API for Spark. It is a hybrid of Apache Spark and Python. As widely known, Spark is a framework from Apache that is known for its unique speed in processing data. On the other hand, Python is a popular general-purpose high-level programming language that extensively finds its usage in machine learning and data analytics.
This means, when you use PySpark, as a user you harness the power of Python and the speed of Spark, both at a time. This post gives you a basic idea of what PySpark is, its features and why Python was chosen for designing this awesome API.
There are four basic concepts that any PySpark learner needs to be familiar with:
- Resilient Distributed Datasets (RDDs)
- Machine Learning
- Data Frames
- PySpark SQL
Let us talk about each of them in simple terms.
Resilient Distributed Datasets (RDDs)
As for any programming language or software, basic building blocks exist for PySpark too. These basic building blocks of PySpark are popularly termed as Resilient Distributed Datasets or, in short, RDDs. As the name says the building blocks are resiliently distributed datasets that exceptionally fault-tolerant and multi-node distributed. As we all know, a data set is a collection of data with values and which is partitioned.
Using RDDs, two main operations are possible:
- You can create a new RDD, which is called a Transformation operation.
- You can apply back the operation on RDD and inform Spark to perform computation and give back the result.
Note that an RDD is immutable in nature; it is just a layer of abstracted data over multi-node distributed collection of data.
When PySpark is innately made of Python, which is widely known for its application in Machine Learning, an extrinsic explanation about PySpark’s usage in Machine Learning does not be needed.
PySpark has a unique library that is specifically designed for performing machine learning operations. The library named Machine Learning Library or MLib, in short, uses machine learning algorithms such as classification, clustering, linear progression, Nearest-neighbor mapping, Kernel density estimation, etc. This library is responsible for PySpark’s feature of capable of working on distributed systems with high scalability.
Like in databases, where data is stored in tables and excel sheets, PySpark stores data in data frames, which are collections of structured or semi-structured data. A data frame is also immutable, owing to the very nature of RDD, the building block of PySpark.
Dataframes are distributed and a user can get the data frame from the RDDs already created or by using the schema and specifying it.
When datasets are all about storing data in a structured or semi-structured format, PySpark SQL is a module that you use to process such datasets. It allows you to read the data from any type of data source and in different file formats. It means with PySpark SQL, you can work on SQL or HiveQL with similar ease. Because of this ease of working with any type of data, Pyspark SQL is growing in popularity among the user community.
Other key points related to working with PySpark are:
- DStream: A DStream solves streaming issues and lets you work on RDDs of similar context. A DStream or Discretized Stream is a key abstraction for streaming in Spark.
- You can process data that is ingested from various sources such as Flume, kinesis, and Kafka into Spark stream using functions such as a map, reduce, join and window.
- Spark streaming provides fault tolerance and stateful operations as desired. You can also integrate your code with MLib, GraphX, and SQL using Spark Streaming.
Why Python for Creating PySpark?
When there are many programming languages available, the combination of Spark only with Python, to create this PySpark API has its own reasons:
- Python is a proven and widely accepted language known for its code readability and ease of maintenance.
- Python’s API is comprehensive, user-friendly and learner-friendly.
- With Python, a wide range of options are available for data visualization compared to its counterparts Scala or Java.
- The rich in feature Python library is another added advantage.
- Python has immense support and usage by its user community across the globe.
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Upcoming Batch Schedule for PySpark Training in Chennai
Besant Technologies provides flexible timings to all our students. Here are the PySpark Training in Chennai Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.
- 30-10-2021Sat (Sat - Sun)Weekend Batch11:00 AM (IST) (Class 3Hrs) / Per SessionGet Fees
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Trainer Profile of PySpark Training in Chennai
Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.
- More than 7+ Years of Experience.
- Trained more than 2000+ students in a year.
- Strong Theoretical & Practical Knowledge.
- Certified Professionals with High Grade.
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- Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
- Trainers have Experienced on multiple real-time projects in their Industries.
- Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc
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Key Features of PySpark Training in Chennai
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When I joined Besant Technologies, I didn’t really expect a lot from it, to be extremely honest. But as time went by, I realised I got from Besant Technologies exactly what I wanted- a healthy environment for learning. Cordial teachers and their valuable lectures make understanding things so much easy. I thank Besant for having been so supportive throughout the course.
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